# library

library(tidyverse)
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library(here)
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#Dataset

 market_sales <- read.csv("Market_Sales.csv", header = TRUE)

(1)Univariate Analysis with 1 numeric and 1 categorical variable = histogram

mark_hist <- ggplot(market_sales,aes(Item_Visibility,fill = Item_Type))+
  geom_histogram(bins = 70)+
  ylim(0,60)+
  xlim(0,0.4)+
  labs(y = "Number Of Items",
       x = "Item Visibility",
       title = "A projection of the visibility per Item",
       subtitle = "By Outlet Type") +
  theme_economist()

ggplotly(mark_hist)

##############################################

(2)Univariate Analysis with 1 numeric variable

a <- ggplot(market_sales, aes(x = Item_Weight))+
  geom_dotplot(aes(fill =Item_Weight),
               fill = "Light Green",alpha =0.5,
               binwidth = 1/4) +
  ylim(0,4)+
  labs(y = "Count",
       x = "Item Weight",
       title = "A projection of Item Weight") +
  theme_classic()

a

##############################################

(3)Bivariate Analysis with 1 numeric and 1 categorical variable: density plot

density(market_sales$Item_Weight)
## 
## Call:
##  density.default(x = market_sales$Item_Weight)
## 
## Data: market_sales$Item_Weight (823 obs.);   Bandwidth 'bw' = 1.098
## 
##        x                y            
##  Min.   : 1.316   Min.   :0.0000259  
##  1st Qu.: 7.148   1st Qu.:0.0151561  
##  Median :12.980   Median :0.0568710  
##  Mean   :12.980   Mean   :0.0428233  
##  3rd Qu.:18.812   3rd Qu.:0.0621254  
##  Max.   :24.644   Max.   :0.0708583
mark_den <- ggplot(market_sales,aes(x = Item_Weight)) + 
  geom_density(fill = "red",alpha =0.5) +
  xlim(0,30)+
  geom_vline(aes(xintercept=mean(Item_Weight)),
             color="blue", linetype="dashed", size=.5)+
  labs(y = "Density",
       x = "Items weight",
       title = "A projection of Fat wise item weight") +
  facet_grid(.~market_sales$Item_Fat_Content)+
  theme_excel()

ggplotly(mark_den) 

##############################################

Outlate Type VS sales

ggplot(market_sales,aes(x = Outlet_Type, y = Item_Outlet_Sales)) +
  geom_jitter(
  aes(shape = Outlet_Type, color = "Red"), 
  position = position_jitter(0.2),
  size = 1.3,color = "blue",alpha = 0.7)+
  
  stat_summary(
    aes(x = Outlet_Type),
    fun.data="mean_sdl",
    geom = "pointrange",  size = 0.7) +
  
  labs(y = "Outlate Sales",
       x = "Outlate Type",
       title = "A projection of Sales per Outlate")+
  theme_fivethirtyeight()

##############################################

Gender VS item quantity // gender wise product pick up quantity

gender <- market_sales$Gender
quantity <- market_sales$Quantity

ggplot(market_sales, aes(y = gender ,
                   x = quantity, 
                   color = gender)) +
  labs(title = "Product pick up quantity by different Gender",
       x = "Quantity", 
       y = "Gender") +
  geom_boxplot(width = .2, 
               fill = "pink", 
               outlier.color = "orange",
               outlier.size = 2)

  theme_clean() +
  theme(legend.position = "none")
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